Feature based high resolution motion estimation from low resolution images captured using an array source

ABSTRACT

Systems and methods in accordance with embodiments of the invention enable feature based high resolution motion estimation from low resolution images captured using an array camera. One embodiment includes performing feature detection with respect to a sequence of low resolution images to identify initial locations for a plurality of detected features in the sequence of low resolution images, where the at least one sequence of low resolution images is part of a set of sequences of low resolution images captured from different perspectives. The method also includes synthesizing high resolution image portions, where the synthesized high resolution image portions contain the identified plurality of detected features from the sequence of low resolution images. The method further including performing feature detection within the high resolution image portions to identify high precision locations for the detected features, and estimating camera motion using the high precision locations for said plurality of detected features.

CROSS-REFERENCE TO RELATED APPLICATIONS

The current application claims priority to U.S. Provisional PatentApplication Ser. No. 61/692,547, entitled “Feature Based High ResolutionMotion Estimation From Low Resolution Images Captured Using an ArraySource” filed Aug. 23, 2012, the disclosure of which is incorporated byreference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to feature detection in digitalimages and more specifically to the use of array cameras and superresolution to improve the performance and efficiency of featuredetection.

BACKGROUND OF THE INVENTION

In digital imaging and computer vision, feature detection is afundamental operation that is typically a preliminary step tofeature-based algorithms such as motion estimation, stabilization, imageregistration, object tracking, and depth estimation. The performance ofthese algorithms depends sensitively on the quality of the feature pointestimates.

Various types of image features include edges, corners or interestpoints, and blobs or regions of interest. Edges are points where thereis a boundary between two image regions, and are usually defined as setsof points in the image which have a strong gradient magnitude. Cornersor interest points can refer to point-like features in an image thathave a local two dimensional structure. A corner can be the intersectionof two edges, or a point for which there are two dominant and differentedge directions in a local neighborhood of the point. An interest pointcan be a point which has a well-defined position and can be robustlydetected, such as a corner or an isolated point of local maximum orminimum intensity. Blobs or regions of interest can describe a type ofimage structure in terms of regions, which often contain a preferredpoint. In that sense, many blob detectors may also be regarded asinterest point operators.

A simple but computationally intensive approach to corner detection isusing correlation. Other methods include the Harris & Stephens cornerdetection algorithm that considers the differential of the corner scorewith respect to direction using the sum of squared differences.

Achieving effective feature detection depends in part on providing highquality data, i.e., high resolution image(s), to the feature detector.

SUMMARY OF THE INVENTION

Systems and methods in accordance with embodiments of the inventionenable feature based high resolution motion estimation from lowresolution images captured using an array camera. One embodimentincludes performing feature detection with respect to a sequence of lowresolution images using a processor configured by software to identifyinitial locations for a plurality of detected features in the sequenceof low resolution images, where the at least one sequence of lowresolution images is part of a set of sequences of low resolution imagescaptured from different perspectives, synthesizing high resolution imageportions from the set of sequences of low resolution images capturedfrom different perspectives using the processor configured by softwareto perform a super-resolution process, where the synthesized highresolution image portions contain the identified plurality of detectedfeatures from the sequence of low resolution images, performing featuredetection within the high resolution image portions to identify highprecision locations for said plurality of detected features using theprocessor configured by software, and estimating camera motion using thehigh precision locations for said plurality of detected features usingthe processor configured by software.

In a further embodiment, wherein the detected features are selected fromthe group consisting of: edges, corners, and blobs.

In another embodiment, performing feature detection with respect to asequence of low resolution images further includes detecting thelocation of features in a first frame from the low resolution sequenceof images, and detecting the location of features in a second frame fromthe low resolution sequence of images.

In a still further embodiment, detecting the location of features in asecond frame from the sequence of low resolution images further includessearching the second frame from the sequence of low resolution images tolocate features detected in the first frame from the sequence of lowresolution images.

In still another embodiment, searching the second frame from thesequence of low resolution images to locate features detected in thefirst frame from the sequence of low resolution images further includesidentifying an image patch surrounding the location of the given featurein the first frame in the sequence of low resolution images, andsearching the second frame in the sequence of low resolution images fora corresponding image patch using a matching criterion.

In a yet further embodiment, the matching criterion involves minimizingan error distance metric.

In yet another embodiment, performing feature detection within the highresolution image portions to identify high precision locations for saidplurality of detected features further comprises searching the highresolution image regions containing the features from the second framein the sequence of low resolution images for features from the firstframe in the sequence of low resolution images using the high resolutionimage regions containing the features from the first frame in the lowresolution sequence of images.

In a further embodiment again, searching the high resolution imageregions containing the features from the second frame in the sequence oflow resolution images for features from the first frame in the sequenceof low resolution images further comprises comparing high resolutionimage regions containing features from the second frame in the sequenceof low resolution images to the high resolution image portionscontaining the features from the first frame in the sequence of lowresolution images using a matching criterion.

In another embodiment again, the matching criterion involves minimizingan error distance metric.

In a further additional embodiment, the processor is part of an arraycamera that further comprises an imager array, the method furthercomprising capturing at least a plurality of the sequences of lowresolution images in the set of sequences of low resolution images fromdifferent perspectives using the imager array.

In another additional embodiment, the high precision locations for saidplurality of detected features estimate feature location at a subpixelprecision relative to the size of the pixels of the frames in thesequence of low resolution images.

In a still yet further embodiment, performing feature detection withrespect to a sequence of low resolution images further comprisesperforming feature detection with respect to a plurality of sequences oflow resolution images, where each sequence is from a differentperspective.

In sill yet another embodiment, the set of sequences of low resolutionimages comprises sequences of low resolution images captured in aplurality of different color channels, and performing feature detectionwith respect to a sequence of low resolution images further comprisesperforming feature detection with respect to a at least one sequence oflow resolution images in each color channel.

Another embodiment includes an imager array, a processor configured bysoftware to control various operating parameters of the imager array. Inaddition, the software further configures the processor to: capture aset of sequences of low resolution images captured from differentperspectives using the imager array; perform feature detection withrespect to one of the set of sequences of low resolution images toidentify initial locations for a plurality of detected features in thesequence of low resolution images; synthesize high resolution imageportions from the set of sequences of low resolution images capturedfrom different perspectives, where the high resolution image portionscontain the identified plurality of detected features from the sequenceof low resolution images; perform feature detection within the highresolution image portions to identify high precision locations for saidplurality of detected features; and estimate camera motion using thehigh precision locations for said plurality of detected features.

In a further embodiment, the detected features are selected from thegroup consisting of: edges, corners, and blobs.

In a still further embodiment, the processor is further configured toperform feature detection with respect to a sequence of low resolutionimages by detecting the location of features in a first frame from thesequence of low resolution images, and detecting the location offeatures in a second frame from the sequence of low resolution images.

In still another embodiment, the processor is further configured bysoftware to detect the location of features in a second frame from thesequence of low resolution images by searching the second frame from thesequence of low resolution images to locate features detected in thefirst frame from the sequence of low resolution images.

In a yet further embodiment, the processor is further configured bysoftware to search a second frame from the sequence of low resolutionimages to locate a given feature detected in the first frame from thesequence of low resolution images by: identifying an image patchsurrounding the location of the given feature in the first frame in thesequence of low resolution images; and searching the second frame in thesequence of low resolution images for a corresponding image patch usinga matching criterion.

In yet another embodiment, the matching criterion involves minimizing anerror distance metric.

In a further embodiment again, the processor is further configured bysoftware to perform feature detection within the high resolution imageportions to identify high precision locations for said plurality ofdetected features by searching the high resolution image regionscontaining the features from the second frame in the sequence of lowresolution images for features from the first frame in the sequence oflow resolution images using the high resolution image regions containingthe features from the first frame in the low resolution sequence ofimages.

In another embodiment again, the processor is further configured bysoftware to search the high resolution image regions containing thefeatures from the second frame in the sequence of low resolution imagesfor features from the first frame in the sequence of low resolutionimages by comparing high resolution image regions containing featuresfrom the second frame in the sequence of low resolution images to thehigh resolution image portions containing the features from the firstframe in the sequence of low resolution images using a matchingcriterion.

In a further additional embodiment, the matching criterion involvesminimizing an error distance metric.

In another additional embodiment, the high precision locations for saidplurality of detected features estimate feature location at a subpixelprecision relative to the size of the pixels of the frames in thesequence of low resolution images.

In a still yet further embodiment, a plurality of the imagers in theimager array sense different wavelengths of light and the set ofsequences of low resolution images comprises sequences of low resolutionimages captured in a plurality of different color channels.

In still yet another embodiment, the processor is further configured bysoftware to perform feature detection with respect to a sequence of lowresolution images by performing feature detection with respect to a atleast one sequence of low resolution images in each color channel.

In a still further embodiment again, the processor is further configuredby software to perform feature detection with respect to a sequence oflow resolution images by performing feature detection with respect to aplurality of sequences of low resolution images, where each sequence isfrom a different perspective.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 a and 1 b are representative low resolution images showing afeature point and the shifted position of the feature point at a latertime.

FIGS. 2 a and 2 b are representative high resolution images showing afeature point and shifted position of the feature point at a later time.

FIG. 3 is a conceptual illustration of an array camera architecture thatcan be used in a variety of array camera configurations in accordancewith embodiments of the invention.

FIG. 4 is a flow chart showing a process for refining featurecorrespondences using high resolution image portions obtained usingsuper resolution processing in accordance with an embodiment of theinvention.

FIGS. 5 a and 5 b are representative low resolution images captured byan array camera showing a feature point and an identified neighborhoodof pixels around the feature point, and the shifted position of thefeature point at a later time.

FIGS. 6 a and 6 b are representative images showing a high resolutionneighborhood of pixels around each feature point obtained using a superresolution process, and the shifted position of the feature point at alater time.

DETAILED DISCLOSURE OF THE INVENTION

Turning now to the drawings, systems and methods for feature based highresolution motion estimation from low resolution images captured usingan array camera in accordance with embodiments of the invention areillustrated. Two images sequentially captured using a legacy camera canreflect a relative displacement due to motion of the camera. This cameramotion, or equivalently, the 3D structure of the scene, can be recoveredfrom the images. The first step toward these goals is to perform featurematching between the two images, the initial step of which is to detectfeatures in one or both images independently. Initial correspondencesare then formed between image features by selecting a patch around eachfeature and minimizing an error distance metric, such as normalizedcross correlation, between the patch and candidate patches in the otherimage. The set of initial correspondences between features can then berefined using a validation procedure such as Random Sample Consensus(RANSAC) for a given motion model.

Array cameras offer a number of advantages and features over legacycameras. An array camera typically contains two or more imagers, each ofwhich receives light through a separate lens system. The imagers operateto capture images of a scene from slightly different perspectives. Theimages captured by these imagers are typically referred to as lowresolution images and super resolution processing can be used tosynthesize a high resolution or super resolution image from a subset ofthe low resolution images. A comparison of a minimum of two lowresolution images can provide parallax information used in superresolution processing. The terms low resolution and high resolution areused relatively and not to indicate any specific image resolution.Imagers in the array may sense different wavelengths of light (e.g.,red, green, blue, infrared), which can improve performance underdifferent lighting conditions and the performance of super resolutionprocessing on images captured using the array. Super resolutionprocesses that can generate higher resolution images using lowresolution images captured by an array camera include those disclosed inU.S. patent application Ser. No. 12/967,807 entitled “Systems andMethods for Synthesizing High Resolution Images Using Super-ResolutionProcesses,” the disclosure of which is hereby incorporated by referencein its entirety.

A sequence of low resolution images captured by the imagers of an arraycamera typically contain temporal displacement between the frames due tocamera motion, as in a legacy camera, but also intra-frame displacementbetween the constituent images of the array (i.e. the low resolutionimages captured by each imager in the array) for each frame due toparallax. Because the offset distances of each imager in the array areknown, the parallax displacement can be calculated and used to registerthe images to perform super resolution processing.

In several embodiments, feature detection can be performed on a sequenceof super resolution images generated using low resolution imagescaptured by the array camera. Performing feature detection in this waycan yield a subpixel estimate of feature positions (i.e. an estimate hasa precision smaller than the size of the pixels of the sensors in thearray camera used to capture the low resolution images). Referring toFIG. 1 a, a low resolution image is shown with pixels forming edges 101and 102. As discussed above, an edge is a feature that can be defined asa boundary between two image regions and a corner can be defined as theintersection of two edges. Point 103 is identified as a corner at theintersection of edges 101 and 102. In a subsequent image at time t+1shown in FIG. 1 b, point 104 is identified as a corner corresponding topoint 103.

By applying a super resolution process, such as one of the processesdescribed in U.S. patent application Ser. No. 12/967,807, the accuracyof the feature detection can be increased. Higher resolution imagesobtained by applying super resolution processing to low resolutionimages including the low resolution images shown in FIGS. 1 a and 1 brespectively are shown in FIGS. 2 a and 2 b. Featured-based algorithmssuch as motion estimation, stabilization, image registration, objecttracking, and depth estimation can be performed on the higher resolutionimages in the same manner that they are performed on lower resolutionimages with the benefit of subpixel accuracy. Performing superresolution processing on the entire image, however, can requireconsiderable computation that uses power and computing resources, andmay not perform optimally on devices with limited processingcapabilities such as mobile platforms.

In many embodiments of the invention, accurate feature detection can beachieved in a computationally efficient manner by initially identifyingthe location of features in low resolution images and then selectivelyperforming super resolution processing to obtain in higher resolutionthe portions of the low resolution images containing the identifiedfeatures. By only performing super resolution processing to obtain theportions of the super resolution images utilized in feature detection,feature detection can be performed at a higher speed (i.e. with fewercomputations) while preserving the benefits of increased accuracy. Inthis way advanced functionality relying upon feature recognition such as(but not limited to) real time image stabilization during video capturecan be performed in a computationally efficient manner. Array camerasand the use of super resolution processes to obtain high resolutionimage portions for performing feature detection in accordance withembodiments of the invention are discussed further below.

Array Camera Architecture

An array camera architecture that can be used in a variety of arraycamera configurations in accordance with embodiments of the invention isillustrated in FIG. 3. The array camera 100 includes an imager array106, which is connected to a processor 108. Imagers 110 in the array 106are evenly spaced in a 5×5 square. In other embodiments, imagers mayhave different spacing or can be arranged in other orientations in thearray. The processor 108 is hardware, software, firmware, or acombination thereof that controls various operating parameters of theimager array 106. The processor 108 can also function to process theimages received from imager array 106 to produce a synthesized higherresolution image using super resolution processes, or transfer theimages to other hardware, software, firmware or a combination thereof toprocess the images. In several embodiments, the array camera includesmemory containing an image processing application that can be utilizedto perform feature based high resolution motion estimation using lowresolution images captured by the array camera utilizing any of thetechniques described below.

Although a specific architecture is illustrated in FIG. 3, any of avariety of architectures that enable the capture of low resolutionimages and application of super resolution processes to produce asynthesized high resolution image can be utilized in accordance withembodiments of the invention.

Obtaining High Resolution Image Portions

In many embodiments of the invention, super resolution is performed toobtain a portion of a high resolution image corresponding to the portionof a low resolution image containing an identified feature. Once thehigh resolution image portion is obtained, feature correspondences thatwere initially determined using the low resolution images can be refinedat the higher resolution. A flow chart illustrating a process 120 forrefining feature correspondences using high resolution image portionsobtained using super resolution processing in accordance with anembodiment of the invention is shown in FIG. 4. Through the followingdiscussion, reference is made to sample images illustrated in FIGS. 5 aand 5 b.

A feature detection algorithm is run on a first low resolution imagecaptured by an imager in an array camera to identify (122) features inthe image. An image from any of the imagers in the array camera can bechosen, so long as the second low resolution image used to performfeature detection is captured from the same imager. In many embodiments,feature detection can be performed with respect to sequences of imagescaptured by multiple cameras to obtain additional information concerningthe location of features. In a number of embodiments, the array cameraincludes cameras that capture images in different color channels and thearray camera performs feature detection with respect to a sequences ofimages captured by cameras in multiple cameras. In certain embodiments,feature detection is performed with respect to a sequence of imagescaptured by at least one camera in each color channel.

As discussed above, the types of features that can be detected in thelow resolution image can include (but are not limited to) edges,corners, and blobs. Typically, a feature detection algorithm identifiesone type of feature based upon the definition of the feature. A cornerdetector such as the Harris and Stephens detection algorithm can be usedto identify corners. In the Harris and Stephens algorithm, an imagepatch is considered over a specified area and shifted. A corner ischaracterized by a large variation in the weighted sum of squareddifferences between the two patches in all directions.

Referring to FIG. 5 a, a low resolution image captured at time t isillustrated. Point 140 in the image is identified as a corner using acorner detection algorithm. Similarly, the feature detection algorithmis run on the second low resolution image to identify (124) features inthe image. A second image at some later time t+1 is depicted in FIG. 5b. Point 142 in the second image is identified as a corner.

In some embodiments of the invention, each feature in the first frame ismatched (i.e. determined to correspond) to a feature in the second framewhere possible. This initial correspondence may not be possible if thefeature has moved out of the second frame or has moved a significantdistance. In other embodiments, the features are not matched in the lowresolution images, but are matched after performing super resolution onportions of the low resolution images (frames).

A neighborhood of pixels around each feature is selected (126) in eachframe. Suitable dimensions for such a neighborhood can be 20 pixels by20 pixels (20×20) to 60 pixels×60 pixels (60×60), although smaller orlarger neighborhoods are possible and may be determined by thelimitations of the computing platform carrying out calculations on theimage. Moreover, the neighborhood can be of any shape and need not besquare. The feature typically can fall within the boundaries of theneighborhood, but need not be centered in the neighborhood.

Referring to FIGS. 5 a and 5 b, a neighborhood of 20×20 pixels 144 isselected around point 140 in the first frame. Similarly, a 20×20neighborhood of pixels 146 is selected around point 142 in the secondframe. For each neighborhood, super resolution processing is performed(128) using parallax information to apply any necessary pixel shifts inthe low resolution images captured by the other imagers in the cameraarray. Super resolution processing can be applied using a subset of thelow resolution images generated by the array camera. As discussed above,an array camera captures images with multiple imagers simultaneously. Asubset (i.e., minimum of two) of low resolution images obtained fromdifferent perspectives provides parallax information that can be used insuper resolution processing. Suitable super resolution processes caninclude (but are not limited to) those disclosed in U.S. patentapplication Ser. No. 12/967,807 (incorporated by reference above).

As discussed above, differences exist in the low resolution imagescaptured by the imagers of a camera array due to the effects ofparallax. In order to synthesize a high resolution image portioncontaining a designated neighborhood, the effects of parallax areaccounted for by determining the parallax between the images andapplying appropriate pixel shifts to the pixels of the low resolutionimages. The pixel shifts may involve moving pixels into the designatedneighborhood and shifting pixels out of the designated neighborhood.Accordingly, although a specific neighborhood of pixels in thesynthesized high resolution image is identified, the super resolutionalgorithm may utilize pixels from the low resolution images that areoutside the neighborhood and exclude pixels from the low resolutionimages within the neighborhood following correcting for parallax.Therefore, the input pixels from the low resolution images utilized toobtain a designated neighborhood of a high resolution image using superresolution processing are not limited to pixels within the designatedneighborhood identified by performing feature detection within theinitial low resolution image pair. The designated neighborhood simplyguides the super resolution process with respect to the low resolutionpixels to utilize to synthesize the portion of the high resolution imagecorresponding to the designated neighborhood. Methods for obtainingdistance and other information using parallax calculations via an arraycamera that can be used in super resolution processing include thosedisclosed in U.S. Patent Application Ser. No. 61/691,666 entitled“Systems and Methods for Parallax Detection and Correction in ImagesCaptured Using Array Cameras,” the disclosure of which is incorporatedby reference herein in its entirety.

The resulting frames are illustrated in FIGS. 6 a and 6 b. Superresolution of the designated neighborhood 144 gives the high resolution40×40 neighborhood 144′ and point 140′ shown in FIG. 6 a. Superresolution of the low resolution neighborhood 146 gives the highresolution 40×40 neighborhood 146′ and point 142′ shown in FIG. 6 b.

In high resolution neighborhood 146′, the position of point 142′ isslightly to the right of where it appears in low resolution neighborhood146. Because super resolution restores the actual high frequency contentof the image, the higher resolution neighborhood provides a “truer”representation of the point's actual position. In many embodiments ofthe invention, the newly calculated positions of points 140′ and 142′within high resolution neighborhoods 144′ and 146′ can be used inmatching (i.e. determining a correspondence between) points 140′ and142′.

Referring to FIGS. 6 a and 6 b, an initial correspondence is formedbetween the first point 140′ and the second point 142′. Correspondencecan be established using a variety of methods. A common method is toselect a patch around each point and minimize an error distance metric,such as (but not limited to) normalized cross correlation, between thepatch and candidate patches in the other image. Point 142′ is thusdetermined to correspond to point 140′ in the previous frame. Othermethods for finding correspondence are known in the art.

Using the initial correspondences, any of a variety of feature-basedalgorithms, including (but not limited to), motion estimation,stabilization, image registration, object tracking, or depth estimation,can be performed on the images. The model that is developed using thefeatures and correspondences (for example, a motion model) can befurther refined using high resolution neighborhoods of pixels thatencompass the relevant features.

The initial correspondences between points 140′ and 142′ are refined(130) using the high resolution neighborhoods (i.e. the high resolutionimage portions). Refinement may be accomplished using a variety ofmethods, including (but not limited to) recomputing a matching metric(e.g., normalized cross-correlation) between a pair of correspondinghigh resolution neighborhoods. Recomputing a matching metric can involvefinding the normalized cross-correlation between high resolutionneighborhoods 144′ and 146′, and using the metric to compute anestimated position of point 142, i.e., future position of point 140′ inthe subsequent frame. In other embodiments, any of a variety of methodscan be utilized appropriate to the requirements of a specificapplication.

A variety of validation procedures can be used such as the RANdom SAmpleConsensus (RANSAC) method for a given model that was formed using theinitial features and correspondences (such as a motion model for motionestimation). The RANSAC method utilizes a set of observed data values, aparameterized model which can be fitted to the observations, andconfidence parameters. A random subset of the original data isiteratively selected as hypothetical inliers and tested by: fitting theparameters of a model to the hypothetical inliers, testing all otherdata against the fitted model, including a point as a hypotheticalinlier if it fits well to the estimated model, keeping the estimatedmodel if sufficiently many points have been classified as hypotheticalinliers, re-estimating the model from the updated set of allhypothetical inliers, and estimating the error of the inliers relativeto the model. Other suitable validation procedures appropriate to aspecific application can also be utilized in accordance with embodimentsof the invention.

Although a specific process is illustrated in FIG. 4, any of a varietyof processes for detecting features in low resolution array frames andrefining feature correspondences using super resolved regions over thefeatures can be utilized in accordance with embodiments of theinvention. While the figures and processes discussed herein depict asingle corner in an image, embodiments of the invention can operate onimages that include multiple features of various types.

Although the description above contains many specificities, these shouldnot be construed as limiting the scope of the invention but as merelyproviding illustrations of some of the presently preferred embodimentsof the invention. Various other embodiments are possible within itsscope.

What is claimed is:
 1. A method for performing feature based highresolution motion estimation from a plurality of low resolution images,comprising: performing feature detection with respect to a sequence oflow resolution images using a processor configured by software toidentify initial locations for a plurality of detected features in thesequence of low resolution images, where the at least one sequence oflow resolution images is part of a set of sequences of low resolutionimages captured from different perspectives; synthesizing highresolution image portions from the set of sequences of low resolutionimages captured from different perspectives using the processorconfigured by software to perform a super-resolution process, where thesynthesized high resolution image portions contain the identifiedplurality of detected features from the sequence of low resolutionimages; performing feature detection within the high resolution imageportions to identify high precision locations for said plurality ofdetected features using the processor configured by software; andestimating camera motion using the high precision locations for saidplurality of detected features using the processor configured bysoftware.
 2. The method of claim 1, wherein the detected features areselected from the group consisting of: edges, corners, and blobs.
 3. Themethod of claim 1, wherein performing feature detection with respect toa sequence of low resolution images further comprises: detecting thelocation of features in a first frame from the low resolution sequenceof images; and detecting the location of features in a second frame fromthe low resolution sequence of images.
 4. The method of claim 3, whereindetecting the location of features in a second frame from the sequenceof low resolution images further comprises searching the second framefrom the sequence of low resolution images to locate features detectedin the first frame from the sequence of low resolution images.
 5. Themethod of claim 4, wherein searching the second frame from the sequenceof low resolution images to locate features detected in the first framefrom the sequence of low resolution images further comprises:identifying an image patch surrounding the location of the given featurein the first frame in the sequence of low resolution images; andsearching the second frame in the sequence of low resolution images fora corresponding image patch using a matching criterion.
 6. The method ofclaim 5, wherein the matching criterion involves minimizing an errordistance metric.
 7. The method of claim 3, wherein performing featuredetection within the high resolution image portions to identify highprecision locations for said plurality of detected features furthercomprises searching the high resolution image regions containing thefeatures from the second frame in the sequence of low resolution imagesfor features from the first frame in the sequence of low resolutionimages using the high resolution image regions containing the featuresfrom the first frame in the low resolution sequence of images.
 8. Themethod of claim 7, wherein searching the high resolution image regionscontaining the features from the second frame in the sequence of lowresolution images for features from the first frame in the sequence oflow resolution images further comprises comparing high resolution imageregions containing features from the second frame in the sequence of lowresolution images to the high resolution image portions containing thefeatures from the first frame in the sequence of low resolution imagesusing a matching criterion.
 9. The method of claim 8, wherein thematching criterion involves minimizing an error distance metric.
 10. Themethod of claim 1, wherein the processor is part of an array camera thatfurther comprises an imager array, the method further comprisingcapturing at least a plurality of the sequences of low resolution imagesin the set of sequences of low resolution images from differentperspectives using the imager array.
 11. The method of claim 1, whereinthe high precision locations for said plurality of detected featuresestimate feature location at a subpixel precision relative to the sizeof the pixels of the frames in the sequence of low resolution images.12. An array camera configured to perform feature based high resolutionmotion estimation from low resolution images captured using the arraycamera, comprising: an imager array; a processor configured by softwareto control various operating parameters of the imager array; wherein thesoftware further configures the processor to: capture a set of sequencesof low resolution images captured from different perspectives using theimager array; perform feature detection with respect to one of the setof sequences of low resolution images to identify initial locations fora plurality of detected features in the sequence of low resolutionimages, synthesize high resolution image portions from the set ofsequences of low resolution images captured from different perspectives,where the high resolution image portions contain the identifiedplurality of detected features from the sequence of low resolutionimages; perform feature detection within the high resolution imageportions to identify high precision locations for said plurality ofdetected features; and estimate camera motion using the high precisionlocations for said plurality of detected features.
 13. The array cameraof claim 12, where the detected features are selected from the groupconsisting of: edges, corners, and blobs.
 14. The array camera of claim12, wherein the processor is further configured to perform featuredetection with respect to a sequence of low resolution images by:detecting the location of features in a first frame from the sequence oflow resolution images; and detecting the location of features in asecond frame from the sequence of low resolution images.
 15. The arraycamera of claim 14, wherein the processor is further configured bysoftware to detect the location of features in a second frame from thesequence of low resolution images by searching the second frame from thesequence of low resolution images to locate features detected in thefirst frame from the sequence of low resolution images.
 16. The arraycamera of claim 15, wherein the processor is further configured bysoftware to search a second frame from the sequence of low resolutionimages to locate a given feature detected in the first frame from thesequence of low resolution images by: identifying an image patchsurrounding the location of the given feature in the first frame in thesequence of low resolution images; and searching the second frame in thesequence of low resolution images for a corresponding image patch usinga matching criterion.
 17. The array camera of claim 16, wherein thematching criterion involves minimizing an error distance metric.
 18. Thearray camera of claim 14, wherein the processor is further configured bysoftware to perform feature detection within the high resolution imageportions to identify high precision locations for said plurality ofdetected features by searching the high resolution image regionscontaining the features from the second frame in the sequence of lowresolution images for features from the first frame in the sequence oflow resolution images using the high resolution image regions containingthe features from the first frame in the low resolution sequence ofimages.
 19. The array camera of claim 18, wherein the processor isfurther configured by software to search the high resolution imageregions containing the features from the second frame in the sequence oflow resolution images for features from the first frame in the sequenceof low resolution images by comparing high resolution image regionscontaining features from the second frame in the sequence of lowresolution images to the high resolution image portions containing thefeatures from the first frame in the sequence of low resolution imagesusing a matching criterion.
 20. The array camera of claim 19, whereinthe matching criterion involves minimizing an error distance metric. 21.The array camera of claim 12, wherein the high precision locations forsaid plurality of detected features estimate feature location at asubpixel precision relative to the size of the pixels of the frames inthe sequence of low resolution images.